A cascaded recognition method for copper rougher flotation working conditions

2020

Complexity

The continuous coupled algebraic Riccati equation (CCARE) has wide applications in control theory and linear systems. In this paper, by a constructed positive semidefinite matrix, matrix inequalities, and matrix eigenvalue inequalities, we propose a new two-parameter-type upper solution bound of the CCARE. Next, we present an iterative algorithm for finding the tighter upper solution bound of CCARE, prove its boundedness, and analyse its monotonicity and convergence. Finally, corresponding numerical examples are given to illustrate the superiority and effectiveness of the derived results.

Section: (2)mentioning

confidence: 99%

Refined Upper Solution Bound of the Continuous Coupled Algebraic Riccati Equation

2020

Complexity

“…To achieve the maximum likelihood of parameters, the log likelihood function should be optimized. Given a vector X = {x (1) , x (2) , • • • , x (n) } and the log likelihood function with K components is defined as follows [27]:…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

“…In the copper flotation process, it is difficult to identify the flotation conditions due to the complex process of copper flotation and the frequently diversified conditions of ore sources [1]. The distribution of concentrate copper grade is different for different ore properties.…”

Section: Introductionmentioning

confidence: 99%

Clustering of Copper Flotation Process Based on the AP-GMM Algorithm

2019

IEEE Access

The clustering of copper flotation process has a significant impact on the performance of operation adjustment. Nowadays, due to the complexity of the copper flotation process, the adjustment of operational variables, which are controlled by operators, is often not regulated properly in time. Therefore, it is necessary to obtain a clustering strategy for the copper flotation process to guide the operators by taking prompt and effective adjustment strategies. Due to the uncertainty of clustering itself, the number of categories and their respective probabilities are needed. Based on affinity propagation (AP) clustering algorithm and gaussian mixture model (GMM), a clustering algorithm is proposed in this paper, which is referred to as AP-GMM. It can get the optimal number of categories and their respective probabilities without giving the number of categories in advance. On this basis, a new category matching theory is constructed. Inspired by rewards and punishments in reinforcement learning, a penalty function is investigated to verify the optimum number of condition categories for copper flotation process. Finally, experiments show the effectiveness and feasibility of the proposed method. INDEX TERMS Copper flotation process, affinity propagation, Gaussian mixture model, clustering.

Dynamic global feature extraction and importance‐correlation selection for the prediction of concentrate copper grade and recovery rate

Zhang

2023

Can J Chem Eng

Large-scale industrial data have brought great challenges to data calculation and analysis. Feature extraction and selection have become one of the research emphases in data mining. To mine the dynamic characteristics of large-scale industrial data, a dynamic global feature extraction (DGFE) method integrating principal component analysis (PCA) and kernel principal component analysis (KPCA) is proposed such that the achieved feature set is not only dynamic but also contains linear and non-linear features. To ensure that the obtained feature set is optimal with the minimum redundancy, a new importancecorrelation-based feature selection (ICFS) method is proposed. To verify the validity and feasibility of the proposed methods, the partial least square (PLS) and least square support vector machine (LSSVM) prediction models for the concentrate copper grade and the recovery rate are established. The effectiveness of the proposed methods is verified through data experiments on a copper flotation industrial process.